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Chinny Sharma

Fordham law professor specializing in AI, technology, and regulatory policy who contributes legal analysis on AI regulation and safety.

Top 3 podcasts with Chinny Sharma

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37 snips
Nov 25, 2024 • 1h 22min

Lawfare Daily: AI Regulation and Free Speech: Navigating the Government’s Tightrope

Eugene Volokh, a First Amendment law expert, Chinny Sharma, who specializes in AI regulation, and Paul Ohm, known for his work on technology and copyright, engage in a thought-provoking discussion on the complexities of regulating AI while protecting free speech. They explore the challenges of managing AI-generated content, the implications for defamation law, and the delicate balance between innovation and accountability. The conversation emphasizes the need for nuanced regulatory frameworks and highlights potential dangers of misinformation in the evolving digital landscape.
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Nov 27, 2025 • 1h 23min

Lawfare Archive: AI Regulation and Free Speech: Navigating the Government’s Tightrope

Join Georgetown law professor Paul Ohm as he leads a thought-provoking discussion with Chinny Sharma, an AI regulation expert, and First Amendment scholar Eugene Volokh. They dive into the complexities of generative AI and its First Amendment implications. Topics range from the concept of 'Cheap Speech' and its impact on free expression to the challenges of regulating AI, including real-world libel cases and privacy issues. The panel also debates the roles of state versus federal regulation, offering insights into the fast-evolving world of AI law.
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Jul 8, 2024 • 52min

Lawfare Daily: Chinny Sharma and Yonathan Arbel on the Promises and Perils of Open-Source AI

Chinny Sharma and Yonathan Arbel discuss open-source AI, covering its origins, implications, and regulatory challenges in the AI community. They explore the benefits of transparency and community collaboration in open-source models, while addressing ethical risks like bias and security concerns. The conversation also touches on the complexities of regulating open-source AI and the need to differentiate between powerful and simple models.

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